Files read on Hugging Face: 2026-10-24

Wan2.2 A14B GGUF files and Mac RAM

Wan2.2 A14B is a two-expert video model with a high-noise and a low-noise expert of about 14B parameters each, 27B in total and 14B active per step. It makes 5-second videos at 480P and 720P, and on a Mac it runs through community MLX packs.

Wan-AI · Updated 2026-10-24
Answer

No published Mac run with a GGUF build yet. The smallest complete GGUF download is 26.29 GB; a download that fits in memory does not prove the model runs.

Smallest Mac with a published run
none yetunknown below
Smallest complete download
26.29 GBQuantStack GGUF Q4_K_M (high and low noise)
Files read
2026-10-24Hugging Face

Apache License 2.0. Commercial use: allowed. Excluded territories: none listed.

Table 1

Which exact GGUF files make a complete build?

Only builds with every required component and an exact probed file size are shown.

QuantStack GGUF Q4_K_M (high and low noise)

gguf · Q4_K_M · 26.29 GB complete

Mac routes: ComfyUI

Exact component files for QuantStack GGUF Q4_K_M (high and low noise)
FileComponentRequiredDownloadSource
HighNoise/Wan2.2-T2V-A14B-HighNoise-Q4_K_M.ggufQuantStack/Wan2.2-T2V-A14B-GGUFHigh-noise expertditYes9.65 GBHugging FaceRead 2026-10-24
LowNoise/Wan2.2-T2V-A14B-LowNoise-Q4_K_M.ggufQuantStack/Wan2.2-T2V-A14B-GGUFLow-noise expertditYes9.65 GBHugging FaceRead 2026-10-24
split_files/text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensorsComfy-Org/Wan-2.2UMT5-XXL text encodertext_encoderYes6.74 GBHugging FaceRead 2026-10-24
split_files/vae/wan_2.1_vae.safetensorsComfy-Org/Wan-2.2Wan2.1 VAEvaeYes0.25 GBHugging FaceRead 2026-10-24

Build sources: QuantStack Wan2.2 T2V A14B GGUF card · Comfy-Org Wan 2.2 repackaged card.

Table 2

Does it fit your Mac's RAM?

Published names the exact measured machine. est. is a memory calculation. unknown means the run verdict is not established, not that the model fails.

Run verdicts by Mac memory tier
Mac RAMComplete buildDownloadRun verdictBasis
8 GBQuantStack GGUF Q4_K_M (high and low noise)Q4_K_M · ComfyUI26.29 GBDoes not fitest.
16 GBQuantStack GGUF Q4_K_M (high and low noise)Q4_K_M · ComfyUI26.29 GBDoes not fitest.
24 GBQuantStack GGUF Q4_K_M (high and low noise)Q4_K_M · ComfyUI26.29 GBDoes not fitest.
32 GBQuantStack GGUF Q4_K_M (high and low noise)Q4_K_M · ComfyUI26.29 GBunknownunknown
48 GBQuantStack GGUF Q4_K_M (high and low noise)Q4_K_M · ComfyUI26.29 GBunknownunknown
64 GBQuantStack GGUF Q4_K_M (high and low noise)Q4_K_M · ComfyUI26.29 GBunknownunknown
96 GBQuantStack GGUF Q4_K_M (high and low noise)Q4_K_M · ComfyUI26.29 GBunknownunknown
128 GBQuantStack GGUF Q4_K_M (high and low noise)Q4_K_M · ComfyUI26.29 GBunknownunknown

How does it rank in blind votes?

#42 of 48 in text to video blind votes, from 10,458 votes.

Quality: blind human votes from wan-v2.2-a14b (arena.ai), CC BY 4.0, snapshot 2026-09-22. Scores are for the Arena checkpoint, not your local quantized build.

Which Mac software routes are listed?

  • ComfyUI

    Official

    Formats: safetensors, gguf. Memory policy: component stages stay resident.

    On Apple Silicon ComfyUI runs on PyTorch MPS, not MLX. GGUF files need the separate city96/ComfyUI-GGUF custom node. Resident size is the sum of the weights, an upper bound: ComfyUI can unload models between stages and publishes no per-model Mac peak.

    Sources: ComfyUI README · ComfyUI system requirements · ComfyUI CLI arguments · ComfyUI-GGUF README

What does the official configuration say?

"The official T2V runner needs a GPU with at least 80GB VRAM. No official Mac memory minimum was found."

Source: Wan2.2 T2V A14B model card · Read 2026-10-24.

What known issues should you check?

  • Known issue 1

    PocketAI packs are community derivatives, not an official Wan-AI release.

    Source: PocketAI Wan2.2 T2V A14B MLX card · Read 2026-10-24

  • Known issue 2

    The PocketAI benchmark carries no run date. The date shown is the repository last-modified date.

    Source: PocketAI Wan2.2 T2V A14B MLX card · Read 2026-10-24

  • Known issue 3

    Wan2.2 A14B needs both experts for a complete run, so every build lists a high-noise and a low-noise file.

    Source: Wan2.2 README · Read 2026-10-24

Sources